Deep learning is a technology used to cluster or classify objects or data. For example, computers cannot distinguish dogs and cats from photographs alone. But a human can easily distinguish those two. To this end, a method called “machine learning” was devised. It is a technique to allow a computer to classify similar things among lots of data inputted into the computer. When a photo of an animal similar to a dog is inputted, the computer will classify it as a dog photo.
There have already been many machine learning algorithms to classify data. For example, a decision tree, a Bayesian network, a support vector machine (SVM), an artificial neural network, etc. have been developed. The deep learning is a descendant of the artificial neural network.
Deep Convolution Neural Networks (Deep CNNs) are at the heart of the remarkable development in deep learning. CNNs have already been used in the 90's to solve the problems of character recognition, but their use has become as widespread as it is now thanks to recent research. These deep CNNs won the 2012 ImageNet image classification tournament, crushing other competitors. Then, the convolution neural network became a very useful tool in the field of the machine learning.
Image segmentation is a method of generating a label image by using an input image. As the deep learning has recently become popular, the segmentation is using the deep learning heavily. The segmentation had been tried with methods using only an encoder, such as a method for generating the label image by convolution operations. Thereafter, the segmentation has been performed with methods using an encoder-decoder configuration for extracting features of the image by the encoder and restoring them as the label image by the decoder. However, it is difficult to get a fine label image only with the encoder-decoder configuration. Therefore, various methods are provided to solve a problem that many of edge parts are missed in the process of encoding and decoding the image, and to reinforce the edge parts in the image or its corresponding feature map.